ENH: Create logistic regression oneDAL object on the fly for predictions after fallbacks

[david-cortes-intel]

Description

This PR changes the logic of logistic regression on GPU to use oneDAL for predictions even if the model was fitted through scikit-learn, by creating a oneDAL object on-the-fly from the fitted coefficients.

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Checklist:

Completeness and readability

• I have commented my code, particularly in hard-to-understand areas.
• Git commit message contains an appropriate signed-off-by string (see CONTRIBUTING.md for details).
• I have resolved any merge conflicts that might occur with the base branch.

Testing

• I have run it locally and tested the changes extensively.
• All CI jobs are green or I have provided justification why they aren't.
• I have extended testing suite if new functionality was introduced in this PR.

[david-cortes-intel]

/intelci: run

[david-cortes-intel]

/azp run Nightly

[azure-pipelines]

Azure Pipelines successfully started running 1 pipeline(s).

[codecov]

Codecov Report

❌ Patch coverage is 19.04762% with 17 lines in your changes missing coverage. Please review.

Files with missing lines	Patch %	Lines
onedal/linear_model/logistic_regression.py	16.66%	10 Missing ⚠️
sklearnex/linear_model/logistic_regression.py	22.22%	7 Missing ⚠️

Flag	Coverage Δ
azure	79.44% <19.04%> (-0.18%)	⬇️
github	81.75% <19.04%> (-0.17%)	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

Files with missing lines	Coverage Δ
sklearnex/linear_model/logistic_regression.py	45.02% <22.22%> (-0.92%)	⬇️
onedal/linear_model/logistic_regression.py	33.82% <16.66%> (-3.68%)	⬇️

... and 1 file with indirect coverage changes

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